Overview

Dataset statistics

Number of variables17
Number of observations43206
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.6 MiB
Average record size in memory136.0 B

Variable types

Categorical4
Numeric13

Alerts

datum has a high cardinality: 532 distinct values High cardinality
häst has a high cardinality: 10235 distinct values High cardinality
dist is highly correlated with lopp_distHigh correlation
lopp_dist is highly correlated with distHigh correlation
dist is highly correlated with lopp_distHigh correlation
lopp_dist is highly correlated with distHigh correlation
dist is highly correlated with lopp_distHigh correlation
lopp_dist is highly correlated with distHigh correlation
dist is highly correlated with lopp_distHigh correlation
lopp_dist is highly correlated with dist and 1 other fieldsHigh correlation
start is highly correlated with lopp_distHigh correlation
delta3 is highly skewed (γ1 = 112.9965609) Skewed
datum is uniformly distributed Uniform
streck has 5157 (11.9%) zeros Zeros

Reproduction

Analysis started2022-01-06 13:19:38.178858
Analysis finished2022-01-06 13:20:07.399068
Duration29.22 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

datum
Categorical

HIGH CARDINALITY
UNIFORM

Distinct532
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size337.7 KiB
2021-02-13
 
100
2020-02-01
 
98
2018-02-24
 
96
2019-02-09
 
96
2020-04-11
 
93
Other values (527)
42723 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2014-12-28
2nd row2014-12-28
3rd row2014-12-28
4th row2014-12-28
5th row2014-12-28

Common Values

ValueCountFrequency (%)
2021-02-13100
 
0.2%
2020-02-0198
 
0.2%
2018-02-2496
 
0.2%
2019-02-0996
 
0.2%
2020-04-1193
 
0.2%
2020-12-3192
 
0.2%
2019-09-2991
 
0.2%
2019-03-3191
 
0.2%
2019-12-2591
 
0.2%
2016-12-2591
 
0.2%
Other values (522)42267
97.8%

Length

2022-01-06T14:20:07.446397image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2021-02-13100
 
0.2%
2020-02-0198
 
0.2%
2018-02-2496
 
0.2%
2019-02-0996
 
0.2%
2020-04-1193
 
0.2%
2020-12-3192
 
0.2%
2019-09-2991
 
0.2%
2019-03-3191
 
0.2%
2019-12-2591
 
0.2%
2016-12-2591
 
0.2%
Other values (522)42267
97.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

avd
Real number (ℝ≥0)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.009165394
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size337.7 KiB
2022-01-06T14:20:07.529450image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.987184787
Coefficient of variation (CV)0.495660466
Kurtosis-1.235141703
Mean4.009165394
Median Absolute Deviation (MAD)2
Skewness-0.000829507796
Sum173220
Variance3.948903379
MonotonicityNot monotonic
2022-01-06T14:20:07.608162image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
46629
15.3%
26432
14.9%
56156
14.2%
76155
14.2%
66093
14.1%
15903
13.7%
35838
13.5%
ValueCountFrequency (%)
15903
13.7%
26432
14.9%
35838
13.5%
46629
15.3%
56156
14.2%
66093
14.1%
76155
14.2%
ValueCountFrequency (%)
76155
14.2%
66093
14.1%
56156
14.2%
46629
15.3%
35838
13.5%
26432
14.9%
15903
13.7%

häst
Categorical

HIGH CARDINALITY

Distinct10235
Distinct (%)23.7%
Missing0
Missing (%)0.0%
Memory size337.7 KiB
ON TRACK PIRATEN
 
91
SUPER ZANTOS
 
62
WEST WING
 
61
ZENIT BRICK
 
55
DISCO VOLANTE
 
55
Other values (10230)
42882 

Length

Max length23
Median length12
Mean length11.82803314
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3838 ?
Unique (%)8.9%

Sample

1st rowALLABALLAKAITOZ
2nd rowARISTOCAT BOKO
3rd rowART ON LINE
4th rowBEAR DANCER
5th rowBY AIR

Common Values

ValueCountFrequency (%)
ON TRACK PIRATEN91
 
0.2%
SUPER ZANTOS62
 
0.1%
WEST WING61
 
0.1%
ZENIT BRICK55
 
0.1%
DISCO VOLANTE55
 
0.1%
MILLIGAN'S SCHOOL54
 
0.1%
NADAL BROLINE54
 
0.1%
VÄSTERBOONTHENEWS52
 
0.1%
NAROLD VOX52
 
0.1%
LINUS BOY49
 
0.1%
Other values (10225)42621
98.6%

Length

2022-01-06T14:20:07.726790image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
boko939
 
1.1%
face888
 
1.0%
global633
 
0.7%
am462
 
0.5%
the454
 
0.5%
kronos443
 
0.5%
sisu427
 
0.5%
de383
 
0.5%
mellby373
 
0.4%
of354
 
0.4%
Other values (9443)79506
93.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

streck
Real number (ℝ≥0)

ZEROS

Distinct91
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.543651345
Minimum0
Maximum90
Zeros5157
Zeros (%)11.9%
Negative0
Negative (%)0.0%
Memory size337.7 KiB
2022-01-06T14:20:07.853472image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q311
95-th percentile33
Maximum90
Range90
Interquartile range (IQR)10

Descriptive statistics

Standard deviation11.81744915
Coefficient of variation (CV)1.383184856
Kurtosis8.504183923
Mean8.543651345
Median Absolute Deviation (MAD)3
Skewness2.615625552
Sum369137
Variance139.6521045
MonotonicityNot monotonic
2022-01-06T14:20:07.973306image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17649
17.7%
05157
11.9%
24640
 
10.7%
33252
 
7.5%
42469
 
5.7%
51915
 
4.4%
61740
 
4.0%
71439
 
3.3%
81237
 
2.9%
91047
 
2.4%
Other values (81)12661
29.3%
ValueCountFrequency (%)
05157
11.9%
17649
17.7%
24640
10.7%
33252
7.5%
42469
 
5.7%
51915
 
4.4%
61740
 
4.0%
71439
 
3.3%
81237
 
2.9%
91047
 
2.4%
ValueCountFrequency (%)
903
 
< 0.1%
894
< 0.1%
884
< 0.1%
876
< 0.1%
866
< 0.1%
854
< 0.1%
843
 
< 0.1%
834
< 0.1%
824
< 0.1%
818
< 0.1%

kr
Real number (ℝ≥0)

Distinct26753
Distinct (%)61.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28363.3917
Minimum0
Maximum806743
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size337.7 KiB
2022-01-06T14:20:08.099922image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5788.25
Q110756.25
median17007
Q329091.75
95-th percentile90072.75
Maximum806743
Range806743
Interquartile range (IQR)18335.5

Descriptive statistics

Standard deviation39653.00173
Coefficient of variation (CV)1.398034556
Kurtosis50.10442242
Mean28363.3917
Median Absolute Deviation (MAD)7727
Skewness5.681766626
Sum1225468702
Variance1572360546
MonotonicityNot monotonic
2022-01-06T14:20:08.228693image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2000017
 
< 0.1%
2500014
 
< 0.1%
1700014
 
< 0.1%
2750013
 
< 0.1%
2400013
 
< 0.1%
1900013
 
< 0.1%
1750013
 
< 0.1%
2250013
 
< 0.1%
1225012
 
< 0.1%
1450012
 
< 0.1%
Other values (26743)43072
99.7%
ValueCountFrequency (%)
03
< 0.1%
7831
 
< 0.1%
10661
 
< 0.1%
11351
 
< 0.1%
12701
 
< 0.1%
12971
 
< 0.1%
14251
 
< 0.1%
14751
 
< 0.1%
14791
 
< 0.1%
16861
 
< 0.1%
ValueCountFrequency (%)
8067431
< 0.1%
7998641
< 0.1%
7734141
< 0.1%
6378211
< 0.1%
6197671
< 0.1%
6051421
< 0.1%
6013481
< 0.1%
5994181
< 0.1%
5959891
< 0.1%
5731251
< 0.1%

spår
Real number (ℝ≥0)

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.691802065
Minimum0
Maximum15
Zeros89
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size337.7 KiB
2022-01-06T14:20:08.347563image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median5
Q38
95-th percentile12
Maximum15
Range15
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.400409229
Coefficient of variation (CV)0.5974222558
Kurtosis-0.9558507376
Mean5.691802065
Median Absolute Deviation (MAD)3
Skewness0.3412506648
Sum245920
Variance11.56278293
MonotonicityNot monotonic
2022-01-06T14:20:08.434539image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
14869
11.3%
24627
10.7%
34578
10.6%
44349
10.1%
53945
9.1%
63846
8.9%
73472
8.0%
93129
7.2%
102880
6.7%
82671
6.2%
Other values (6)4840
11.2%
ValueCountFrequency (%)
089
 
0.2%
14869
11.3%
24627
10.7%
34578
10.6%
44349
10.1%
53945
9.1%
63846
8.9%
73472
8.0%
82671
6.2%
93129
7.2%
ValueCountFrequency (%)
15109
 
0.3%
14122
 
0.3%
13133
 
0.3%
121904
4.4%
112483
5.7%
102880
6.7%
93129
7.2%
82671
6.2%
73472
8.0%
63846
8.9%

dist
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct48
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2187.699787
Minimum1600
Maximum3700
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size337.7 KiB
2022-01-06T14:20:08.552995image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1600
5-th percentile1640
Q12120
median2140
Q32160
95-th percentile3140
Maximum3700
Range2100
Interquartile range (IQR)40

Descriptive statistics

Standard deviation393.6790235
Coefficient of variation (CV)0.1799511184
Kurtosis0.6173047981
Mean2187.699787
Median Absolute Deviation (MAD)20
Skewness0.7284415231
Sum94521757
Variance154983.1735
MonotonicityNot monotonic
2022-01-06T14:20:08.670682image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
214019726
45.7%
16405870
 
13.6%
26405414
 
12.5%
21602554
 
5.9%
21002024
 
4.7%
16091896
 
4.4%
31401172
 
2.7%
2660753
 
1.7%
3160715
 
1.7%
2000522
 
1.2%
Other values (38)2560
 
5.9%
ValueCountFrequency (%)
160076
 
0.2%
16091896
 
4.4%
16405870
13.6%
164610
 
< 0.1%
1660244
 
0.6%
168014
 
< 0.1%
180064
 
0.1%
2000522
 
1.2%
201158
 
0.1%
202012
 
< 0.1%
ValueCountFrequency (%)
37007
 
< 0.1%
368014
 
< 0.1%
366022
 
0.1%
364024
 
0.1%
322017
 
< 0.1%
320076
 
0.2%
3180516
1.2%
3160715
1.7%
31401172
2.7%
312078
 
0.2%

lopp_dist
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct23
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2184.22527
Minimum1600
Maximum3640
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size337.7 KiB
2022-01-06T14:20:08.778680image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1600
5-th percentile1640
Q12100
median2140
Q32140
95-th percentile3140
Maximum3640
Range2040
Interquartile range (IQR)40

Descriptive statistics

Standard deviation390.5266046
Coefficient of variation (CV)0.1787941061
Kurtosis0.5671308319
Mean2184.22527
Median Absolute Deviation (MAD)0
Skewness0.7100259929
Sum94371637
Variance152511.0289
MonotonicityNot monotonic
2022-01-06T14:20:08.879108image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
214022751
52.7%
26406326
 
14.6%
16406128
 
14.2%
31402385
 
5.5%
21002037
 
4.7%
16091896
 
4.4%
2000545
 
1.3%
2600199
 
0.5%
3100185
 
0.4%
2950125
 
0.3%
Other values (13)629
 
1.5%
ValueCountFrequency (%)
160076
 
0.2%
16091896
 
4.4%
16406128
 
14.2%
164610
 
< 0.1%
180064
 
0.1%
2000545
 
1.3%
201158
 
0.1%
21002037
 
4.7%
212023
 
0.1%
214022751
52.7%
ValueCountFrequency (%)
364067
 
0.2%
31402385
 
5.5%
312063
 
0.1%
3100185
 
0.4%
300048
 
0.1%
2950125
 
0.3%
290026
 
0.1%
288013
 
< 0.1%
265024
 
0.1%
26406326
14.6%

start
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size337.7 KiB
0
30104 
1
13102 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
030104
69.7%
113102
30.3%

Length

2022-01-06T14:20:08.981589image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-06T14:20:09.186977image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
030104
69.7%
113102
30.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

kön
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size337.7 KiB
v
21057 
s
11313 
h
10836 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowv
2nd rowv
3rd rowv
4th rowv
5th rowv

Common Values

ValueCountFrequency (%)
v21057
48.7%
s11313
26.2%
h10836
25.1%

Length

2022-01-06T14:20:09.243772image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-06T14:20:09.303110image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
v21057
48.7%
s11313
26.2%
h10836
25.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ålder
Real number (ℝ≥0)

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.812317734
Minimum0
Maximum15
Zeros6
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size337.7 KiB
2022-01-06T14:20:09.362982image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q14
median5
Q37
95-th percentile10
Maximum15
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.922386433
Coefficient of variation (CV)0.3307435211
Kurtosis1.224132744
Mean5.812317734
Median Absolute Deviation (MAD)1
Skewness1.002909639
Sum251127
Variance3.695569598
MonotonicityNot monotonic
2022-01-06T14:20:09.446735image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
510435
24.2%
48976
20.8%
68336
19.3%
75404
12.5%
83354
 
7.8%
32531
 
5.9%
91831
 
4.2%
101127
 
2.6%
11594
 
1.4%
12312
 
0.7%
Other values (5)306
 
0.7%
ValueCountFrequency (%)
06
 
< 0.1%
2113
 
0.3%
32531
 
5.9%
48976
20.8%
510435
24.2%
68336
19.3%
75404
12.5%
83354
 
7.8%
91831
 
4.2%
101127
 
2.6%
ValueCountFrequency (%)
1523
 
0.1%
1449
 
0.1%
13115
 
0.3%
12312
 
0.7%
11594
 
1.4%
101127
 
2.6%
91831
 
4.2%
83354
7.8%
75404
12.5%
68336
19.3%

pris
Real number (ℝ≥0)

Distinct137
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean174481.5374
Minimum0
Maximum2000000
Zeros391
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size337.7 KiB
2022-01-06T14:20:09.575694image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10
Q1100000
median110000
Q3150000
95-th percentile550000
Maximum2000000
Range2000000
Interquartile range (IQR)50000

Descriptive statistics

Standard deviation226942.001
Coefficient of variation (CV)1.30066484
Kurtosis26.21582035
Mean174481.5374
Median Absolute Deviation (MAD)15000
Skewness4.605041541
Sum7538649304
Variance5.15026718 × 1010
MonotonicityNot monotonic
2022-01-06T14:20:09.729853image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000012000
27.8%
1250007307
16.9%
1100004481
 
10.4%
1500003762
 
8.7%
2000003621
 
8.4%
2500001755
 
4.1%
300000767
 
1.8%
220000766
 
1.8%
50000742
 
1.7%
1571
 
1.3%
Other values (127)7434
17.2%
ValueCountFrequency (%)
0391
0.9%
1571
1.3%
2297
0.7%
3222
 
0.5%
4172
 
0.4%
5135
 
0.3%
6110
 
0.3%
792
 
0.2%
886
 
0.2%
980
 
0.2%
ValueCountFrequency (%)
2000000189
 
0.4%
195376010
 
< 0.1%
182294412
 
< 0.1%
160900019
 
< 0.1%
150000098
 
0.2%
140000070
 
0.2%
120000047
 
0.1%
115000024
 
0.1%
1000000532
1.2%
97688023
 
0.1%

senast
Real number (ℝ≥0)

Distinct287
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.1539601
Minimum0
Maximum364
Zeros15
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size337.7 KiB
2022-01-06T14:20:09.872015image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8
Q112
median15
Q322
95-th percentile46
Maximum364
Range364
Interquartile range (IQR)10

Descriptive statistics

Standard deviation22.98094684
Coefficient of variation (CV)1.086366181
Kurtosis58.51501722
Mean21.1539601
Median Absolute Deviation (MAD)5
Skewness6.53741868
Sum913978
Variance528.1239175
MonotonicityNot monotonic
2022-01-06T14:20:10.001157image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
145288
 
12.2%
212996
 
6.9%
102675
 
6.2%
112259
 
5.2%
152203
 
5.1%
132152
 
5.0%
122028
 
4.7%
171952
 
4.5%
71777
 
4.1%
91739
 
4.0%
Other values (277)18137
42.0%
ValueCountFrequency (%)
015
 
< 0.1%
37
 
< 0.1%
46
 
< 0.1%
521
 
< 0.1%
6121
 
0.3%
71777
4.1%
81515
3.5%
91739
4.0%
102675
6.2%
112259
5.2%
ValueCountFrequency (%)
3641
 
< 0.1%
3631
 
< 0.1%
3531
 
< 0.1%
3501
 
< 0.1%
3491
 
< 0.1%
3481
 
< 0.1%
3431
 
< 0.1%
3411
 
< 0.1%
3373
< 0.1%
3361
 
< 0.1%

delta1
Real number (ℝ≥0)

Distinct365
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.16495394
Minimum0
Maximum750
Zeros238
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size337.7 KiB
2022-01-06T14:20:10.137465image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q112
median16
Q324
95-th percentile72
Maximum750
Range750
Interquartile range (IQR)12

Descriptive statistics

Standard deviation34.29788409
Coefficient of variation (CV)1.362922585
Kurtosis52.06388749
Mean25.16495394
Median Absolute Deviation (MAD)5
Skewness5.941616662
Sum1087277
Variance1176.344853
MonotonicityNot monotonic
2022-01-06T14:20:10.261036image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
144429
 
10.3%
212513
 
5.8%
112319
 
5.4%
102173
 
5.0%
132051
 
4.7%
152025
 
4.7%
121914
 
4.4%
71761
 
4.1%
171719
 
4.0%
181552
 
3.6%
Other values (355)20750
48.0%
ValueCountFrequency (%)
0238
 
0.6%
113
 
< 0.1%
25
 
< 0.1%
364
 
0.1%
486
 
0.2%
5173
 
0.4%
6382
 
0.9%
71761
4.1%
81335
3.1%
91502
3.5%
ValueCountFrequency (%)
7501
< 0.1%
6692
< 0.1%
6381
< 0.1%
6061
< 0.1%
5661
< 0.1%
5291
< 0.1%
5021
< 0.1%
4941
< 0.1%
4921
< 0.1%
4781
< 0.1%

delta2
Real number (ℝ≥0)

Distinct413
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.48039624
Minimum0
Maximum1918
Zeros221
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size337.7 KiB
2022-01-06T14:20:10.393423image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q112
median16
Q324
95-th percentile80
Maximum1918
Range1918
Interquartile range (IQR)12

Descriptive statistics

Standard deviation41.78708879
Coefficient of variation (CV)1.578038652
Kurtosis238.2780869
Mean26.48039624
Median Absolute Deviation (MAD)5
Skewness10.25221281
Sum1144112
Variance1746.160789
MonotonicityNot monotonic
2022-01-06T14:20:10.521414image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
144268
 
9.9%
212430
 
5.6%
112306
 
5.3%
132180
 
5.0%
102156
 
5.0%
151996
 
4.6%
121867
 
4.3%
71825
 
4.2%
171819
 
4.2%
161595
 
3.7%
Other values (403)20764
48.1%
ValueCountFrequency (%)
0221
 
0.5%
114
 
< 0.1%
26
 
< 0.1%
348
 
0.1%
496
 
0.2%
5157
 
0.4%
6413
 
1.0%
71825
4.2%
81308
3.0%
91507
3.5%
ValueCountFrequency (%)
19181
< 0.1%
17071
< 0.1%
11541
< 0.1%
9231
< 0.1%
9001
< 0.1%
8691
< 0.1%
8681
< 0.1%
8301
< 0.1%
8061
< 0.1%
7901
< 0.1%

delta3
Real number (ℝ≥0)

SKEWED

Distinct412
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.3095172
Minimum0
Maximum12343
Zeros268
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size337.7 KiB
2022-01-06T14:20:10.652187image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q112
median16
Q325
95-th percentile86
Maximum12343
Range12343
Interquartile range (IQR)13

Descriptive statistics

Standard deviation73.00619524
Coefficient of variation (CV)2.673287657
Kurtosis18768.53172
Mean27.3095172
Median Absolute Deviation (MAD)5
Skewness112.9965609
Sum1179935
Variance5329.904544
MonotonicityNot monotonic
2022-01-06T14:20:10.776854image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
144195
 
9.7%
212386
 
5.5%
112281
 
5.3%
132132
 
4.9%
102129
 
4.9%
151932
 
4.5%
121859
 
4.3%
71802
 
4.2%
171680
 
3.9%
161602
 
3.7%
Other values (402)21208
49.1%
ValueCountFrequency (%)
0268
 
0.6%
111
 
< 0.1%
26
 
< 0.1%
365
 
0.2%
496
 
0.2%
5178
 
0.4%
6410
 
0.9%
71802
4.2%
81351
3.1%
91574
3.6%
ValueCountFrequency (%)
123431
< 0.1%
19181
< 0.1%
17071
< 0.1%
12461
< 0.1%
9601
< 0.1%
8121
< 0.1%
7641
< 0.1%
7561
< 0.1%
7501
< 0.1%
7281
< 0.1%

delta4
Real number (ℝ≥0)

Distinct422
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.41725686
Minimum0
Maximum1130
Zeros323
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size337.7 KiB
2022-01-06T14:20:10.904578image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q112
median16
Q325
95-th percentile89
Maximum1130
Range1130
Interquartile range (IQR)13

Descriptive statistics

Standard deviation42.08231272
Coefficient of variation (CV)1.53488414
Kurtosis76.40869123
Mean27.41725686
Median Absolute Deviation (MAD)5
Skewness6.770800943
Sum1184590
Variance1770.921044
MonotonicityNot monotonic
2022-01-06T14:20:11.027061image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
144135
 
9.6%
212335
 
5.4%
112219
 
5.1%
132120
 
4.9%
102103
 
4.9%
151943
 
4.5%
121882
 
4.4%
71756
 
4.1%
171754
 
4.1%
161588
 
3.7%
Other values (412)21371
49.5%
ValueCountFrequency (%)
0323
 
0.7%
117
 
< 0.1%
28
 
< 0.1%
365
 
0.2%
4114
 
0.3%
5193
 
0.4%
6424
 
1.0%
71756
4.1%
81350
3.1%
91586
3.7%
ValueCountFrequency (%)
11301
< 0.1%
9331
< 0.1%
9231
< 0.1%
9211
< 0.1%
8591
< 0.1%
8431
< 0.1%
7711
< 0.1%
7631
< 0.1%
7581
< 0.1%
7341
< 0.1%

Interactions

2022-01-06T14:20:05.072102image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:43.867903image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:45.474799image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:47.273687image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:49.229800image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:51.079787image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:52.674229image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:54.529718image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:56.181695image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:58.033722image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:59.708437image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:20:01.518628image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:20:03.425760image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:20:05.194160image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:43.992307image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:45.594704image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:47.403847image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:49.369874image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:51.207694image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:52.806459image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:54.653198image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:56.312936image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:58.164548image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:59.833442image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:20:01.648573image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:20:03.547816image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:20:05.308707image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:44.110139image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:45.704582image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:47.524360image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:49.501600image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:51.324854image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:52.929687image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:54.770990image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:56.437088image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:58.284118image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:20:00.105618image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:20:01.768623image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:20:03.664159image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:20:05.440165image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:44.237535image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:45.833310image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:47.659270image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:49.645046image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:51.453153image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:53.067976image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:54.904037image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:56.573015image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:58.419949image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:20:00.237253image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:20:01.909905image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:20:03.800211image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:20:05.558372image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:44.359627image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:45.978543image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:47.848322image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:49.777014image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:51.571936image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:53.198894image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:55.034403image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:56.856414image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:58.547859image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:20:00.367786image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:20:02.043737image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:20:03.928750image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:20:05.678298image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:44.477648image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:46.095219image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:47.980542image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:49.893646image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:51.686466image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:53.320257image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:55.153685image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:56.980227image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:58.666572image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:20:00.486758image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:20:02.170040image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:20:04.044388image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:20:05.807950image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:44.604636image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:46.233177image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:48.151020image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:50.026943image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:51.813727image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:53.606189image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:55.288296image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:57.114545image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:58.798471image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:20:00.620295image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:20:02.315833image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:20:04.177486image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:20:05.931545image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:44.727717image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:46.363342image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:48.312071image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:50.304241image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:51.935193image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:53.733568image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:55.414571image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:57.243503image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:58.925747image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:20:00.748573image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:20:02.458448image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:20:04.303638image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:20:06.062864image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:44.857555image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:46.499376image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:48.473311image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:50.438288image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:52.066417image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:53.869308image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:55.547827image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:57.382320image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:59.058242image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:20:00.885279image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:20:02.604820image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:20:04.437891image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:20:06.188974image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:44.980172image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:46.782882image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:48.631346image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:50.565435image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:52.186223image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:53.999551image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:55.677783image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:57.513952image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:59.185487image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:20:01.014938image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:20:02.739289image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:20:04.566667image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:20:06.314730image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:45.102400image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:46.902386image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:48.789814image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:50.693099image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:52.307319image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:54.131811image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:55.804904image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:57.644557image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:59.316155image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:20:01.143364image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:20:02.868883image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:20:04.695065image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:20:06.443604image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:45.228590image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:47.030636image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:48.941944image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:50.826605image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:52.436153image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:54.265096image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:55.936806image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:57.780717image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:59.450861image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:20:01.270314image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:20:03.004785image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:20:04.828182image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:20:06.713279image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:45.355245image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:47.157888image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:49.087103image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:50.951715image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:52.556835image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:54.398238image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:56.063213image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:57.909321image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:19:59.582085image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:20:01.397144image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:20:03.131341image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-06T14:20:04.948709image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-01-06T14:20:11.298000image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-01-06T14:20:11.513334image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-01-06T14:20:11.686363image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-01-06T14:20:11.841710image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-01-06T14:20:11.968495image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-01-06T14:20:06.903223image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-01-06T14:20:07.212815image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

datumavdhäststreckkrspårdistlopp_diststartkönålderprissenastdelta1delta2delta3delta4
02014-12-281ALLABALLAKAITOZ5.021018.06.02100.02100.00v6125000.021.019.017.010.018.0
12014-12-281ARISTOCAT BOKO7.023466.012.02100.02100.00v7125000.08.07.024.014.011.0
22014-12-281ART ON LINE23.020696.02.02100.02100.00v7125000.09.06.019.041.015.0
32014-12-281BEAR DANCER48.027477.01.02100.02100.00v4125000.018.025.014.013.08.0
42014-12-281BY AIR5.030589.05.02100.02100.00v7125000.016.0230.014.049.014.0
52014-12-281FREDRIK LADAY1.017496.08.02100.02100.00v6125000.016.06.031.067.021.0
62014-12-281LADY LEE0.013805.07.02100.02100.00s7125000.010.010.024.08.056.0
72014-12-281LORD ROCKET6.010191.03.02100.02100.00v9125000.08.07.036.015.013.0
82014-12-281MAGNIFIK GELJ0.06650.011.02100.02100.00v9125000.07.010.021.021.017.0
92014-12-281NAVIGATOR SIB1.014176.010.02100.02100.00v7125000.08.015.016.018.09.0

Last rows

datumavdhäststreckkrspårdistlopp_diststartkönålderprissenastdelta1delta2delta3delta4
431962022-01-017SOUL TAN4.017569.03.02140.02140.00v74.017.087.011.037.019.0
431972022-01-017TALE OF JACKS11.015884.04.02140.02140.00h711.014.018.019.013.014.0
431982022-01-017LASS REVENUE4.012230.05.02140.02140.00v84.015.010.018.021.012.0
431992022-01-017ARCH LANE44.031680.06.02140.02140.00v844.014.011.037.017.013.0
432002022-01-017MELLBY HOFFA1.029092.07.02140.02140.00v61.013.010.053.0135.05.0
432012022-01-017VICTORY TOPLINE6.022881.08.02140.02140.00v86.010.018.08.09.0228.0
432022022-01-017AXL BROWN6.010576.09.02140.02140.00v86.022.022.027.013.09.0
432032022-01-017I'LL FIXIT1.010574.010.02140.02140.00v91.023.09.013.012.07.0
432042022-01-017BIG SHOT1.016593.011.02140.02140.00v61.015.014.014.012.023.0
432052022-01-017DOLLAR DOC6.034115.012.02140.02140.00v56.017.019.019.014.021.0